Agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks

Abstract

AbstractDue to the high computational complexity of traditional convolutional neural networks, the execution time is long and the computational cost is too high. In this paper, we propose a deep separable convolutional neural network with attention mechanism added to improve the classification accuracy and generalization ability of hyperspectral images. The network uses separable convolution combined with residual connections to construct residual units with fewer parameters and adds an attention mechanism layer at the end of the network, which helps to improve the overall performance of the model. So this model has stronger generalization ability now, shorter computation time, and stronger network performance. Finally, the overall accuracy of the model in this paper is 98.48%, 99.1% and 97.40% on the Salinas dataset and the more newly proposed Wuhan Longkou and Wuhan Hanchuan datasets, respectively. It proves that the model has better generalization ability and can complete the calculation in a shorter time. Improving the classification accuracy of hyperspectral images like the Wuhan Longkou dataset is important for agricultural development.KeywordsResidual NetworkSeparable convolutionConvolutional neural networkAttention mechanismAgricultural hyperspectrum